Run a complete design workflow -- from establishing context through four progressive design levels (Capabilities, Components, Interactions, Contracts) to an approved blueprint. Composes knowledge-priming, context-anchoring, learning-harvest, collaborative-judgment, design-first, architecture, and domain-driven-design into one process. Handles both new features (create context doc) and resuming existing work (load context doc). Level 5 (Implementation) is delegated to code-forge. Use when starting a design, planning architecture, or when the user says 'design a feature', 'blueprint', 'start designing', 'plan the architecture', or 'let's design before coding'.
日本語の概要は準備中です。原文の説明を表示しています。
techygarg/lattice☆ 2042026年10月10日 更新
Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Detect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data. Uses Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering to avoid artificial cell populations. Use when identifying and removing doublets from scRNA-seq data.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Installs 425 bioinformatics skills covering sequence analysis, RNA-seq, single-cell, variant calling, metagenomics, structural biology, and 56 more categories. Use when setting up bioinformatics capabilities or when a bioinformatics task requires specialized skills not yet installed.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Applies ACMG/AMP 2015 framework with ClinGen SVI specifications, Tavtigian 2018/2020 Bayesian point system, Abou Tayoun 2018 PVS1 decision tree, Pejaver 2022 calibrated PP3/BP4 thresholds for REVEL/BayesDel/AlphaMissense, Brnich 2020 PS3/BS3 OddsPath, Walker 2023 SpliceAI splicing framework, and AMP/ASCO/CAP 2017 tumor tiers. Use when classifying germline variants P / LP / VUS / LB / B, applying VCEP-specific CSpec rules, computing Whiffin BS1, or assigning cancer Tier I-IV per Li 2017.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Publication-quality PDF report generation using Typst templates. Produces professional scientific reports with colored section bands, styled tables, figure captions, callout boxes, and page headers/footers.
日本語の概要は準備中です。原文の説明を表示しています。
BioTender-max/awesome-bio-agent-skills☆ 2022026年7月2日 更新
Identify the technology stack powering any website — frameworks, CMS, analytics, hosting, CDN, payment processors, and marketing tools. Useful for competitive intelligence, sales prospecting, and technology landscape research.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Conduct comprehensive market research combining web search, academic sources, news coverage, and industry data. Produces market sizing signals, competitive landscape overviews, trend analysis, and evidence-backed market opportunity assessments.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Scrape web pages, crawl sites, extract structured data, and capture screenshots through SandBase. Use when asked to extract content from URLs, crawl websites, get structured data from pages, take screenshots, or convert web pages to markdown.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Search videos, analyze channels, extract captions, read comments, and discover trends on YouTube through SandBase. Use when asked for YouTube research, video discovery, channel analysis, content strategy research, comment sentiment, or video transcript extraction.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Analyze hiring patterns, job market demand, salary signals, and talent competition across LinkedIn and Apollo. Covers job posting analysis, company hiring velocity, role requirement trends, and competitive talent landscape mapping.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Research companies with employee reviews, product reviews, industry classification, and competitive landscape through Akta. Covers company enrichment, employee satisfaction signals, product feedback, industry mapping, and news monitoring.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Build evidence-backed SEO keyword insights and strategy using SandBase-managed search, keyword, site-analysis, and SERP capabilities. Use when asked to discover or prioritize keywords, assess search demand or ranking feasibility, map intent and topics, find competitor gaps, plan organic-search landing pages or content, or analyze a website's SEO opportunities.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Apply obsidian bases as a bounded agent workflow while preserving user intent, privacy, and host capabilities. Use when the user explicitly requests obsidian bases behavior or files.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Apply obsidian markdown as a bounded agent workflow while preserving user intent, privacy, and host capabilities. Use when the user explicitly requests obsidian markdown behavior or files.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Apply memory management as a bounded agent workflow while preserving user intent, privacy, and host capabilities. Use when the user explicitly requests memory management behavior or files.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Apply adhd-friendly communication as a bounded agent workflow while preserving user intent, privacy, and host capabilities. Use when the user explicitly requests adhd-friendly communication behavior or files.
日本語の概要は準備中です。原文の説明を表示しています。
sandbaseai/sandbase-skills☆ 2012026年9月26日 更新
Use when we want to turn a just-finished Formax workflow (e.g. commands, overlays, tools, hooks, permissions, UI parity) into a reusable Codex Skill under .codex/skills, including scaffolding, guardrails, and the minimum test checklist.
日本語の概要は準備中です。原文の説明を表示しています。
yusifeng/formax☆ 1942026年7月25日 更新
Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification over Firecrawl's paper index — largely biomedical and life-science literature from PubMed, bioRxiv, and medRxiv, plus arXiv preprints in CS, physics, and math. Use when the user wants a literature review, systematic review, survey of studies, paper summary, research landscape, or sourced synthesis from scholarly and industry publications, including clinical, drug, gene, disease, epidemiology, and public-health topics. Prefer this over a general web-research workflow whenever the evidence base is published papers rather than web pages.
日本語の概要は準備中です。原文の説明を表示しています。
firecrawl/firecrawl-workflows☆ 1932026年10月16日 更新